Do Option Characteristics Predict the Underlying Stock Returns in the Cross-Section?

• 成果类型: Article; Early Access
• 署名作者: Neuhierl, Andreas; Tang, Xiaoxiao; Varneskov, Rasmus T.; Zhou, Guofu
• 署名单位: Purdue University System; Purdue University; University of Texas System; University of Texas Dallas; Copenhagen Business School; Danish Finance Institute; Washington University (WUSTL)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.04720
• 发表日期: 2025
• 关键词: Asset pricing; factor models; high-dimensional methods; option characteristics

摘要

We provide a comprehensive analysis of option-implied information for predicting the cross-section of stock returns. Based on large sets of firm and option characteristics and using traditional portfolio sorts and modern high-dimensional methods, we find that option information matters. However, in contrast to existing studies, there are only a few option characteristics that have significant incremental predictive power after controlling for the large set of firm characteristics. Further analysis reveals that the strongest option characteristics are associated with asset mispricing, future tail return realizations, and short-selling costs. Our findings are consistent with models of informed trading and limits to arbitrage.